A density-based enhancement to dominant sets clustering

IET Computer Vision

Volumn 7, Issue 5, 2013, Pages 354-361

  Hou, Jian ^a   Xu, E ^a,c   Liu, Wei Xue ^a   Xia, Qi ^b   Qi, Nai Ming ^b  

^a BOHAI UNIVERSITY   (China)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER-MERGING; DENSITY-BASED; DISTANCE MEASURE; DOMINANT SET; GRAPH THEORETIC APPROACH; OVER SEGMENTATION;

GRAPH THEORY; IMAGE SEGMENTATION;

CLUSTERING ALGORITHMS;

EID: 84886714334     PISSN: 17519632     EISSN: 17519640     Source Type: Journal    
DOI: 10.1049/iet-cvi.2013.0072     Document Type: Article

References (34)

1. Bradley, P.S., Mangasarian, O.L.: 'Clustering via concave minimization'. Proc. Neural Information Processing Systems, Denver, USA, 1997, pp. 368-374
2. Arthor, D., Vassilvitskii, S.: 'K-means ++ : the advantages of careful seeding'. Proc. ACM-SIAM Symp. Discrete Algorithms, New Orleans, USA, January 2007, pp. 1027-1035
3. Zhang, T., Ramakrishnan, R., Birch, L.M.: 'An efficient data clustering method for very large databases'. Proc. ACM SIGMOD Int. Conf. Management of Data, Montreal, Canada, June 1996, pp. 103-114
4. Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: 'A density-based algorithm for discovering clusters in large spatial databases with noise'. Proc. Int. Conf. Knowledge Discovery and Data Mining, Portland, USA, August 1996, pp. 226-231
5. Wang, W., Yang, J., Sting, M.R.: 'A statistical information grid approach to spatial data mining'. Proc. VLDB Conf., Athens, Greece, August 1997, pp. 186-195
6. Shi, J., Malik, J.: 'Normalized cuts and image segmentation', IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 167-172
7. Dhillon, I.: 'Co-clustering documents and words using bipartite spectral graph partitioning'. Proc. Int. Conf. Knowledge Discovery and Data Mining, San Francisco, USA, August 2001, pp. 269-274
8. Ding, C., He, X., Zha, H., Gu, M., Simon, H.: 'A min-max cut algorithm for graph partitioning and data clustering'. Proc. Int. Conf. Data Mining, San Jose, USA, November 2011, pp. 107-114
9. Ng, A., Jordan, M., Weiss, Y.: 'On spectral clustering: analysis and an algorithm'. Proc. Neural Information Processing Systems, Vancouver, Canada, December 2002, pp. 849-856
10. Belkin, M., Niyogi, P.: 'Laplacian eigenmaps for dimensionality reduction and data representation', Neural Comput., 2003, 15, (6), pp. 1373-1396
11. Smyth, P.: 'Clustering with Monte Carlo cross-validation'. Proc. Int. Conf. Knowledge Discovery and Data Mining, Portland, USA, August 1996, pp. 126-133
12. Fraley, C., Raftery, A.E.: 'How many clusters? which clustering method? answers via model-based cluster analysis', Comput. J., 1998, 41, (8), pp. 578-588
13. Hansen, M.H., Yu, B.: 'Model selection and the principle of minimum descriptor length', J. Am. Stat. Assoc, 1998, 96, (454), pp. 746-774
14. Ray, S., Turi, R.H.: 'Determination of number of clusters in k-means clustering and application in color image segmentation'. Proc. Int. Conf. Advances in Pattern Recognition and Digital Techniques, Calcutta, India, December 1999, pp. 137-143
15. Tibshirani, R., Walther, G., Hastie, T.: 'Estimating the number of clusters in a data set via the gap statistic', J. Royal Stat. Soc. B, Stat. Methodol., 2001, 63, (2), pp. 411-423
16. Monti, S., Tamayo, P., Mesirov, J., Golub, T.: 'Consensus clustering: a resampling based method for class discovery and visualization of gene expression microarray data', Mach. Learn., 2003, 52, (1-2), pp. 91-118
17. Tibshirani, R., Walther, G., Botstein, D., Brown, P.: 'Cluster validation by prediction strength', J. Computat. Graph. Stat., 2005, 14, (3), pp. 511-528
18. Evanno, G., Regnaut, S., Goudet, J.: 'Detecting the number of clusters of individuals using the software structure: a simulation study', Mol. Ecol., 2005, 14, (8), pp. 2611-2620
19. Pavan, M., Pelillo, M.: 'Dominant sets and pairwise clustering', IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (1), pp. 167-172
20. Torsello, A., Bulo, S.R., Pelillo, M.: 'Beyond partitions: allowing overlapping groups in pairwise clustering'. Proc. Int. Conf. Pattern Recognit., Tempa, USA, December 2008, pp. 1-4
21. Yang, X.W., Liu, H.R., Laecki, L.J.: 'Contour-based object detection as dominant set computation', Pattern Recognit., 2012, 45, (5), pp. 1927-1936
22. Frommlet, F.: 'Tag snp selection based on clustering according to dominant sets found using replicator dynamics', Adv. Data Anal. Classif., 2010, 4, (1), pp. 65-83
23. Hamid, R., Maddi, S., Johnson, A.Y., Bobick, A.F., Essa, I.A., Isbell, C.: 'A novel sequence representation for unsupervised analysis of human activities', Artif. Intell., 2009, 173, (14), pp. 1221-1244
24. Wang, M., Ye, Z., Wang, Y., Wang, S.: 'Dominant sets clustering for image retrieval', Signal Process., 2008, 88, (11), pp. 2843-2849
25. Hou, J., Pelillo, M.: 'A simple feature combination method based on dominant sets', Pattern Recognit., 2013, 46, (11), pp. 3129-3139
26. Brendan, J.F., Delbert, D.: 'Clustering by passing messages between data points', Science, 2007, 315, pp. 972-976
27. Wu, Z., Leahy, R.: 'An optimal graph theoretic approach to data clustering: theory and its application to image segmentation', IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (11), pp. 1101-1113
28. Pavan, M., Pelillo, M.: 'A graph-theoretic approach to clustering and segmentation'. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, Madison, USA, June 2003, pp. 145-152
29. Rota Bulò, S., Pelillo, M., Bomze, I.M.: 'Graph-based quadratic optimization: a fast evolutionary approach', Comput. Vis. Image Underst., 2011, 115, (7), pp. 984-995
30. Gionis, A., Mannila, H., Tsaparas, P.: 'Clustering aggregation', ACM Trans. Knowl. Discov. Data, 2007, 1, (1), pp. 1-30
31. Chang, H., Yeung, D.Y.: 'Robust path-based spectral clustering', Pattern Recognit., 2008, 41, (1), pp. 191-203
32. Jain, A., Law, M.: 'Data clustering: a user's dilemma', (LNCS 3776), 2005, pp. 1-10
33. Fu, L., Medico, E.: 'FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data'. BMC Bioinf., 2007, 8, (1), pp. 1-15
34. Zahn, C.T.: 'Graph-theoretical methods for detecting and describing gestalt clusters', IEEE Trans. Comput., 1971, 100, (1), pp. 68-86